NLP empowers non-technical users to access data using natural language queries, and GPT-3 and chatbots create easy-to-use interfaces, revolutionizing self-service data access.

In today’s data-driven world, businesses depend on data to make informed decisions. Nevertheless, traditional methods of accessing and analyzing data often require technical skills and knowledge, making a barrier for non-technical users. To interrupt down this barrier, tools and semantic layers have been created to supply self-service data access. Nevertheless, these solutions require significant engineering time to establish and maintain.

That’s where natural language processing (NLP) is available in. With NLP, non-technical users can access data using natural language queries, without requiring any technical knowledge. By leveraging the ability of NLP, businesses can break down barriers and empower their employees to make data-driven decisions.

One approach to implement NLP for self-service data is to make use of solutions powered by GPT-3. With GPT-3, you may train a model to grasp natural language queries and generate SQL queries to extract data from databases. In this manner, non-technical users can access data without having to learn SQL or other technical skills.

By training a chatbot with a big dataset of prompts and corresponding SQL queries, you may enable it to generate accurate and relevant SQL queries from natural language queries. This may provide an easy-to-use interface for non-technical users to access data without requiring significant engineering time.

I built a small app using streamlit, OpenAI API, BigQuery, and Plotly. This app is showing exactly how NLP can solve the issue of self-service analytics.

For instance, let’s say you could have a BigQuery table of video game sales data, and you desire to know which platform has the best global sales. With our Streamlit app, you may simply select the table, type in your request (“Give me the sum of sales by genre order desc”), and select a graph style to visualise the outcomes. Our app uses NLP to generate the SQL query, retrieve the information, and display it in a user-friendly chart. This permits non-technical users to access priceless insights from data while not having to know SQL or depend on technical teams to run queries.

Please find below an example:

As you may see, I only need to select my table, select the kind of graph after which ask my query. The query generated looks like this:

SELECT Genre, SUM(Global_Sales) AS Total_Sales FROM project.dataset.vgsales GROUP BY Genre ORDER BY Total_Sales DESC;

Overall, NLP has the potential to revolutionize self-service data access and break down barriers for non-technical users. By leveraging the ability of chatbots and GPT-3, businesses can empower their employees to make data-driven decisions without requiring significant technical skills or engineering time.

Please note that the Streamlit app presented in this text is a proof of concept and wishes further improvements to be usable in a production environment. Also, we’d wish to make clear that the instance request we utilized in the app is simply for demonstration purposes and can’t be used with external APIs attributable to data privacy and security concerns. In a real-world scenario, additional measures should be taken to make sure data privacy and security when accessing sensitive information.